Constituting abrupt magnetic flux density change for power density improvement in electromagnetic energy harvesting

Li, Zhongjie; Liu, Yong; Yin, Peilun; Peng, Yan; Luo, Jun; Xie, Shaorong; Pu, Huayan

doi:10.1016/j.ijmecsci.2021.106363

Cited by 108 publications

(31 citation statements)

References 59 publications

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“…The electromagnetic vibration generators (EMVG) produce power from the ambient sources through mechanical vibrational force, where structure acts as a magnetic circuit [33,34]. To extract electrical charge from the EMVG, this depends on the number of coil turns, damping, flux connection slope, and load impedance [35][36][37]. However, as compared to conventional electromagnetic generators, the micro-electromagnetic power generator produces high power.…”

Section: Mounting Of Electromagnetic Vibration Generatorsmentioning

confidence: 99%

Review of Power Converter Impact of Electromagnetic Energy Harvesting Circuits and Devices for Autonomous Sensor Applications

et al. 2021

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The demand for power is increasing due to the rapid growth of the population. Therefore, energy harvesting (EH) from ambient sources has become popular. The reduction of power consumption in modern wireless systems provides a basis for the replacement of batteries with the electromagnetic energy harvesting (EMEH) approach. This study presents a general review of the EMEH techniques for autonomous sensor (ATS) applications. Electromagnetic devices show great potential when used to power such ATS technologies or convert mechanical energy to electrical energy. As its power source, this stage harvests ambient energy and features a self-starting and self-powered process without the use of batteries. Therefore, it consumes low power and is highly stable for harvesting energy from the environment with low ambient energy sources. The review highlights EMEH circuits, low power EMEH devices, power electronic converters, and controllers utilized in numerous applications, and described their impacts on energy conservation, benefits, and limitation. This study ultimately aims to suggest a smart, low-voltage electronic circuit for a low-power sensor that harvests electromagnetic energy. This review also focuses on various issues and suggestions of future EMEH for low power autonomous sensors.

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Section: Mounting Of Electromagnetic Vibration Generatorsmentioning

confidence: 99%

Review of Power Converter Impact of Electromagnetic Energy Harvesting Circuits and Devices for Autonomous Sensor Applications

et al. 2021

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“…In addition, we surrounded this laminar flow by Homogenized multi-turn Coil as a conductor model that represent electromagnetic field [24][25][26][27]. The coil is Numeric, and coil magnetic vector potential is Quadratic, with current (A) to coil excitation as we need to see the Magnetic Flux Density MFD [28][29][30][31][32]. Our model is a Cartesian geometry {x, y, z} with a main parameter listed in table 1.…”

Section: Model Constructionmentioning

confidence: 99%

Simulation: Early Detection of Brain Vessels Stroke by Applying Electromagnetic Waves Non-Invasively

Elshahat¹,

Ashraf²,

Waleed³

et al. 2021

IJRITCC

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Introduction: Early recognition of stroke with its two types Ischemic and Hemorrhagic, is one of the most crucial research points, commonly used methods are CT- (computerized tomography), and MRI- (Magnetic resonance imaging). These techniques cause a delay in the detection of the condition, which causes permanent disability. The main reason behind the fatal consequences of stroke is the delay of detection. Therefore, this research paper aims to early detection of the type of stroke without delay until the appropriate diagnosis of each type is made, and then the appropriate treatment without delay. Method: Using a non-invasive and fast technique to determine the stroke type by wave, we simulate and design a vessel containing a liquid as a laminar flow with the same density and velocity of blood, and it was surrounded by a Homogenized multi-turn coil consisting of (n) turns to represent the magnetic field, using specific frequency (HZ) with Electrical field in coil current (A) to see the changing in magnetic flux density (MFD), Depending on the changes in MFD, the flow of blood in laminar flow can be affected by clotting (Ischemic) or Hemorrhagic (cutting) in our vessel designed. We have built three different scenarios to apply the technique which are: First: Normal Scenario (where the blood in vessel has no problem), second: clotting (ischemic, where the vessel blocked in specific three position) and Third: Cutting (Hemorrhagic, where the vessel cut in certain nine positions). Results: This paper presents-through our own design-the studying of applying the electromagnetic waves on blood inside the vessel to detect the stroke type in our three scenarios (normal, ischemic three positions or hemorrhagic nine positions), Studying the magnetic field and laminar flow. This study covered in three areas. First: coil geometry analysis, Second: stationary, and Third: frequency domain. through the changes in Magnetic Flux Density -MFD- waves. The results were promising and distinct for distinguishing between the three scenarios which are normal, ischemic (3 positions) and hemorrhagic (9 positions) the results of MFD are: 0.09 to 3.3*10^-3, 0.08 to 3.15*10^-4, 0.15 to 6.2*10^-3 respectively.

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“…In smart grid network, power cables have many dynamic modules for improving industrial power management and smart environments to create and support a safe condition for power lines in electricity flow networks [7,8]. Today, high-voltage power cable examination is a critical manner for getting time and human side effects [9].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Convolutional Neural Network and Relief-F Algorithm for Fault Power Line Recognition in Internet of Things-Based Smart Grids

Yuqing

2022

Wireless Communications and Mobile Computing

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Today, energy management based on the digitalization of smart grids by the Internet of Things (IoT) is an emerging paradigm for power line systems. There are several environmental hazards to break down high-voltage power cables such as lightning, severe voltage fluctuations, and incorrect design of electric field distribution. So, identifying faulty high-voltage power lines is one of the most emerging challenges in smart grids to avoid disruption of the power distribution networks. This paper presents a new hybrid Convolutional Neural Network and Relief-F (CNN-RF) algorithm for an energy-aware collaborative learning approach to detect power line systems in smart grids. This hybrid approach ensures the stability and reliability of the defective power line system and improves the energy efficiency of the smart grids. This approach can detect the defective power line recognition using damaged power line images concerning automatic monitoring using Unmanned Aerial Vehicle (UAV) control system and IoT communications. By applying UAV control system and IoT communications on gathering damaged power line images, human faults and environmental hazards for extra data transmission are avoided. Experimental results show that the proposed CNN-RF model represents a high accuracy rate of 92.2% for recognizing damaged power lines. Also, the precision of damaged line detection ratio is higher than other prediction methods by the rate of 92.5%. Finally, the performance of the damaged line prediction approach in the CNN-RF method has a daily minimum cost in the IoT-based smart grids.

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Constituting abrupt magnetic flux density change for power density improvement in electromagnetic energy harvesting

Cited by 108 publications

References 59 publications

Review of Power Converter Impact of Electromagnetic Energy Harvesting Circuits and Devices for Autonomous Sensor Applications

Review of Power Converter Impact of Electromagnetic Energy Harvesting Circuits and Devices for Autonomous Sensor Applications

Simulation: Early Detection of Brain Vessels Stroke by Applying Electromagnetic Waves Non-Invasively

A Hybrid Convolutional Neural Network and Relief-F Algorithm for Fault Power Line Recognition in Internet of Things-Based Smart Grids

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